"""
模型监控集成模块
集成Prometheus等监控工具，实时监控模型性能
"""

from typing import Dict, Any, Optional
from prometheus_client import Counter, Histogram, Gauge, Summary
import time


class ModelMonitoring:
    """模型监控类"""
    
    def __init__(self):
        """初始化监控指标"""
        # 模型调用计数器
        self.model_calls = Counter(
            'model_calls_total',
            'Total number of model calls',
            ['model_name', 'model_type']
        )
        
        # 模型响应时间直方图
        self.model_response_time = Histogram(
            'model_response_time_seconds',
            'Model response time in seconds',
            ['model_name', 'model_type'],
            buckets=(0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 30.0, 60.0, float('inf'))
        )
        
        # 模型准确率仪表盘
        self.model_accuracy = Gauge(
            'model_accuracy_score',
            'Model accuracy score (0-1)',
            ['model_name', 'model_type']
        )
        
        # 模型错误率仪表盘
        self.model_error_rate = Gauge(
            'model_error_rate',
            'Model error rate (0-1)',
            ['model_name', 'model_type']
        )
        
        # 模型选择时间摘要
        self.model_selection_time = Summary(
            'model_selection_time_seconds',
            'Time spent selecting models',
            ['task_type']
        )
        
        # 模型选择计数器
        self.model_selections = Counter(
            'model_selections_total',
            'Total number of model selections',
            ['task_type', 'selected_model']
        )
    
    def record_model_call(self, model_name: str, model_type: str):
        """记录模型调用
        
        Args:
            model_name: 模型名称
            model_type: 模型类型
        """
        self.model_calls.labels(model_name=model_name, model_type=model_type).inc()
    
    def record_model_response_time(self, model_name: str, model_type: str, response_time: float):
        """记录模型响应时间
        
        Args:
            model_name: 模型名称
            model_type: 模型类型
            response_time: 响应时间（秒）
        """
        self.model_response_time.labels(model_name=model_name, model_type=model_type).observe(response_time)
    
    def record_model_accuracy(self, model_name: str, model_type: str, accuracy: float):
        """记录模型准确率
        
        Args:
            model_name: 模型名称
            model_type: 模型类型
            accuracy: 准确率（0-1）
        """
        self.model_accuracy.labels(model_name=model_name, model_type=model_type).set(accuracy)
    
    def record_model_error(self, model_name: str, model_type: str):
        """记录模型错误
        
        Args:
            model_name: 模型名称
            model_type: 模型类型
        """
        # 这里可以增加错误计数逻辑
        pass
    
    def record_model_error_rate(self, model_name: str, model_type: str, error_rate: float):
        """记录模型错误率
        
        Args:
            model_name: 模型名称
            model_type: 模型类型
            error_rate: 错误率（0-1）
        """
        self.model_error_rate.labels(model_name=model_name, model_type=model_type).set(error_rate)
    
    def record_model_selection(self, task_type: str, selected_model: str, selection_time: float):
        """记录模型选择
        
        Args:
            task_type: 任务类型
            selected_model: 选择的模型
            selection_time: 选择耗时（秒）
        """
        self.model_selection_time.labels(task_type=task_type).observe(selection_time)
        self.model_selections.labels(task_type=task_type, selected_model=selected_model).inc()
    
    def get_metrics(self) -> Dict[str, Any]:
        """获取当前监控指标
        
        Returns:
            包含当前监控指标的字典
        """
        # 注意：在实际应用中，Prometheus客户端会自动暴露这些指标
        # 这里仅用于演示目的
        return {
            "model_calls": "See Prometheus metrics",
            "model_response_time": "See Prometheus metrics",
            "model_accuracy": "See Prometheus metrics",
            "model_error_rate": "See Prometheus metrics",
            "model_selection_time": "See Prometheus metrics",
            "model_selections": "See Prometheus metrics"
        }


# 全局监控实例
model_monitoring = ModelMonitoring()